Create README.md

README.md

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----
-title: Food Image Crowdsourcing
-emoji: 🦀
-colorFrom: indigo
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.43.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: This app lets users participate in research on food habits
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🍽️ Food Image Crowdsourcing App
+
+A Streamlit application that allows users to upload food images for AI model training. These images will be used to fine-tune datasets for food image recognition to annotate uploaded food pictures with caloric and nutrient values.
+
+## 📋 Features
+
+- User authentication via Firebase
+- Image upload and processing
+- Token reward system for contributors
+- AWS S3 storage for images
+- DynamoDB for metadata storage
+
+## 🚀 Deployment to Hugging Face Spaces
+
+### Prerequisites
+
+1. [Hugging Face](https://huggingface.co/) account
+2. AWS account with S3 and DynamoDB
+3. Firebase project for authentication
+
+### Step 1: Set up AWS Resources
+
+1. Create an S3 bucket: `food-image-crowdsourcing`
+2. Create a DynamoDB table: `image_metadata` with primary key `image_id`
+3. Create an IAM user with permissions for S3 and DynamoDB
+4. Note your AWS Access Key and Secret Key
+
+### Step 2: Set up Firebase Authentication
+
+1. Create a new Firebase project at [Firebase Console](https://console.firebase.google.com/)
+2. Enable Email/Password authentication
+3. Generate a new private key for your service account (Project settings > Service accounts > Generate new private key)
+4. Save the JSON file
+
+### Step 3: Create a New Hugging Face Space
+
+1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
+2. Click "Create new Space"
+3. Choose a name for your Space
+4. Select "Streamlit" as the SDK
+5. Choose visibility (Public or Private)
+6. Click "Create Space"
+
+### Step 4: Upload Files to the Space
+
+Upload the following files to your Space:
+- `app.py`
+- `requirements.txt`
+- `PARTICIPATION_GUIDELINES.md`
+- `TOKEN_REWARDS.md`
+- `TERMS_OF_SERVICE.md`
+
+You can do this via the Hugging Face web interface or using Git.
+
+### Step 5: Configure Secrets
+
+1. Go to your Space's Settings tab
+2. Under "Repository secrets", add the following secrets:
+   - `AWS_ACCESS_KEY`: Your AWS access key
+   - `AWS_SECRET_KEY`: Your AWS secret key
+   - `AWS_REGION`: Your AWS region (e.g., `us-east-1`)
+   - `S3_BUCKET_NAME`: Your S3 bucket name (e.g., `food-image-crowdsourcing`)
+   - `DYNAMODB_TABLE`: Your DynamoDB table name (e.g., `image_metadata`)
+   - `FIREBASE_CONFIG`: Paste the entire content of your Firebase service account JSON file
+
+### Step 6: Deploy and Run
+
+1. Your Space should automatically build and deploy when you push your changes
+2. If not, you can manually rebuild from the Settings tab
+3. Once deployed, you can access your app at `https://huggingface.co/spaces/your-username/your-space-name`
+
+## 📄 Important Files
+
+- `app.py`: The main Streamlit application
+- `requirements.txt`: Python dependencies
+- `PARTICIPATION_GUIDELINES.md`: Rules for user participation
+- `TOKEN_REWARDS.md`: Explanation of the token reward system
+- `TERMS_OF_SERVICE.md`: Legal terms and conditions
+
+## 🔄 Local Development
+
+To run this application locally:
+
+1. Clone the repository
+2. Install dependencies: `pip install -r requirements.txt`
+3. Create a `.env` file with the required environment variables
+4. Run the app: `streamlit run app.py`
+
+## 📦 Required Environment Variables
+
+```
+AWS_ACCESS_KEY=your_aws_access_key
+AWS_SECRET_KEY=your_aws_secret_key
+AWS_REGION=your_aws_region
+S3_BUCKET_NAME=your_s3_bucket_name
+DYNAMODB_TABLE=your_dynamodb_table_name
+FIREBASE_CONFIG=your_firebase_json_config
+```
+
+## 📊 Data Model
+
+### S3 Object Structure
+
+Images are stored in S3 with the following path pattern:
+```
+{user_id}/{timestamp}_{image_id}.jpg
+```
+
+### DynamoDB Schema
+
+Each image entry in DynamoDB contains:
+- `image_id` (Primary Key): UUID for the image
+- `user_id`: ID of the uploading user
+- `upload_timestamp`: When the image was uploaded
+- `food_name`: Name of the food
+- `portion_size`: Size/weight of the portion
+- `portion_unit`: Unit of measurement
+- `cooking_method`: How the food was prepared
+- `ingredients`: List of main ingredients
+- `s3_path`: Path to the image in S3
+- `tokens_awarded`: Number of tokens awarded for this upload
+
+## 🛠️ Future Enhancements
+
+1. Image tagging with machine learning
+2. Nutritional information estimation
+3. Social sharing features
+4. Mobile app integration
+5. Enhanced analytics dashboard
+
+## 📝 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.